A repeated measures analysis (Bayesian or otherwise) with dependent measurements
Hi JASP experts,
This is a general question about assumptions that, I think, is applicable to both Bayesian and traditional repeated measures ANOVAs.
@Cherie and I have eye-movement data of participants searching through a set of books. I'll simplify the design a bit for the sake of the discussion, but we can provide the actual data if that's useful.
There are two book categories, A and B. For each trial we have quantified the gaze duration on each of the categories, giving two measures per trial. These measures are dependent, because if they look at A then they cannot look at B. In other words, high gaze durations for A are predictive (though not perfectly) of low gaze durations for B, and vice versa.
Then we have an experimental condition with two levels, X and Y. We're interested in whether this condition affects gaze duration, such that participants look more at A in condition X and more at B in condition Y.
An intuitive appealing way to analyze this is with a repeated measures, in which we treat book category as a factor, so we have a 2 (book category: A, B) × 2 (condition: X, Y) design with gaze duration as dependent measure. And then we'd be interested in the book category × condition interaction (not in the main effects of book category or condition).
Now here's where things get tricky.
- I'm pretty sure that it's ok to look at the main effect of condition on gaze duration, because X and Y are independent.
- I suspect that it's problematic to look at the main effect of book category on gaze duration, because A and B are not independent. But I'm not 100% sure about this.
- And what about the book category × condition interaction. Is that valid? And if not, how would we ideally analyze a dataset like this?
I find it hard to wrap my head around this issue, so I really hope that someone can shed some light on this for us!
Sebastiaan
Comments
How dependent are A and B?
If they are completely dependent (say 100% gaze = GazeA + GazeB), than no need to put both measurements into the model - the intercept will give an indication for both, the main effect for condition (X/Y) will actually be the interaction, with the conditional means in X and Y the effect of A vs. B.
You can also convert your measurements to that this ^ is true:
DV = GazeA / (GazeA + GazeB)
Hi MSB,
Thanks for your reply. 😄
The gaze durations on A and B are somewhat dependent, but not perfectly. (If they were, we could indeed recode it without losing data.) Basically, there are three possibilities:
And the measures that we have are proportional gaze durations for A and B across a trial, which are generally values in the 0.1 to 0.3 range.
So to restate the main question: Given this scenario, is it acceptable to treat this as a 2 (book category: A, B) × 2 (condition: X, Y) design with gaze duration as dependent measure?
Cheers!
Sebastiaan
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Hmmm... Given your data and design, probably the most correct analysis would be a multinomial logistic regression...
But let's stick to an ANOVA-like design.
It seems %A and %B are dependent (negatively). You can deal with this dependance in two ways:
This would mean you use a liner-mixed model with a random intercept by trial (accounting for the differences between trials in %neither-A-nor-B), and a random slope for Book by trial (accounting for the negative dependence within each trial).
In a
lme4type formula, your model would look like this:percent_looking ~ Book * Condition + (1 + Book | Trial) + (1 + ... | Subject)(
(...| Subject)indicating any within-subject effects)Hi MSB,
Thanks for this. That makes sense.
Our design is actually a little more complicated than what I described here, in the sense that there are four book categories, and four conditions. Does that make any difference for your proposed approach?
Cheers!
Sebastiaan
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I don't think this should matter.
But, upon further reflection, the
lme4formula should be:to account for the fact the trials are nested in subjects (and a random book effect per subject)
or, if you suspect there may be any random effect for trials across subjects:
percent_looking ~ Book * Condition + (1 + Book | Trial:Subject) + (1 | Trial) + (1 + Book + ... | Subject)Good luck!
> to account for the fact the trials are nested in subjects (and a random book effect per subject)
Right, I was thinking about that too, but I was unsure how to indicate that.
Thanks for all your help!
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interesting problem!